
where \(\phi \)  is the ReLU function [1]}.
We then sample a hop-size \(\Delta t\)  from the normal distribution with mean \(\mu _{t^\prime }\) : \(\Delta t \sim \mathcal {N}(\mu _{t^\prime }, \sigma )\) .
We leave the standard deviation \(\sigma \)  as a hyperparameter, though in principle it can also be learned by the model.
To ensure \(\Delta t \ge 0\) , we take the absolute value of \(\Delta t\) , which is a common approach in similar scenarios [2]}.
To compute the new candidate halting time, we add the hop-size to the current candidate halting time: \(t^\prime \mathrel {{+}{=}} \Delta t\) .
